## [1] "/home/guanshim/Documents/gitlab/Omics_Integration/DataRaw/hiv_infected_un"

1 Preprocessing

# source functions source to get the
# load_filtered_micro_level function to get clr of RA
source(paste0(dir, "Code/5_29_Generate_filtered_Data_Microbiome.R"))
# clean and transform transcriptome data, subset of genes
source(paste0(dir, "Code/6_5_clean_transcriptome.R"))
# clinical data
source(paste0(dir, "Code/6_5_clean_clinical.R"))
# diagnostic plots and tables
source(paste0(dir, "Code/ref_plots.R"))
# outliers
source(paste0(dir, "Code/outliers.R"))
## [1] "Regularized Log Transformation will be applied!"
## [1] "Check Samples, Match: "
## [1] TRUE
## [1] "No gene list provided, will use the whole Transcriptome"
# wrappers
source(paste0(dir, "Code/wrappers.R"))
# run smccnet
source(paste0(dir, "Code/put_together.R"))
# subset global rna-seq by mean, var
source(paste0(dir, "Code/generate_genelist.R"))

########## Datasets ############# phenotype contains ID ####
clin <- rescaled_cli()
## [1] "Regularized Log Transformation will be applied!"
## [1] "Check Samples, Match: "
## [1] TRUE
## [1] "No gene list provided, will use the whole Transcriptome"
CD14 <- clin %>% select(CD14)
anyNA(CD14)
## [1] FALSE
LPS <- clin %>% select(LPS)
n_na <- which(is.na(LPS))
######### Transcriptome ############### Transcriptome ######
rna_isgs <- as.data.frame(read.delim(paste0(dir, "DataRaw/hiv_infected_un/coreISG"))) %>% 
    rescaled_rna(., rlog = T)
## [1] "Regularized Log Transformation will be applied!"
## [1] "Check Samples, Match: "
## [1] TRUE
## [1] "Use a subset of genes"
# the isgs data
isgs_rlog <- rna_isgs[[1]]
# names
colnames(isgs_rlog) <- rna_isgs[[2]]$Symbol
######## genes beta
rna_genesbeta <- as.data.frame(read.delim(paste0(dir, "DataRaw/hiv_infected_un/genesbeta"))) %>% 
    rescaled_rna(., rlog = T)
## [1] "Regularized Log Transformation will be applied!"
## [1] "Check Samples, Match: "
## [1] TRUE
## [1] "Use a subset of genes"
genesbeta_rlog <- rna_genesbeta[[1]]
colnames(genesbeta_rlog) <- rna_genesbeta[[2]]$Symbol
#### global filtered #######3
mean_cut <- 100
var_cut <- 50
filtered_rna <- filter_rescale_rna(mean_cut, var_cut, T)
## [1] "Check Samples, Match: "
## [1] TRUE
## [1] "Regularized Log Transformation will be applied!"
## [1] "Check Samples, Match: "
## [1] TRUE
## [1] "Use a subset of genes"
filtered_rlog <- filtered_rna[[1]]
colnames(filtered_rlog) <- filtered_rna[[2]]$Symbol
print(ncol(filtered_rlog))
## [1] 1905
#### Microbiome ##########
micro_data <- load_filtered_micro_level_samples("genus", prevalence = 40, 
    RA = 2, wd = "Ubuntu")
## Found "Unclassified" category in input data
## Created new "Other" category.
## Converted 35400 counts to "Other" otu category.
## Remaining OTUS: 270  (Including "Other").
## 
## Prevalence cutoff: 40% (i.e., at least 40% of libaries must be represented to keep OTU)
## Found 'Other' category in input data.
## Collapsed 195 OTUs to 'Other' in OTU table.
## Converted 80457 counts to 'Other' in OTU table.
## Remaining OTUs: 75  (Including 'Other').
## 
## Relative abundance cutoff: 2 % (i.e., at least one library must have RA > 2 % to keep OTU).
## Found "Other" category in input data.
## Collapsed 21 OTUs to "Other" otu category.
## Converted 47123 counts to "Other" otu category.
## Remaining OTUS: 54  (Including "Other").
## 
## Contains 27 subjects/libraries from Explicet OTU file.
micro_clr <- micro_data[[2]] %>% as.data.frame()
# rescale to mean 0 and variance 1
mibi <- rescale_microbiome(micro_clr)

print("Before outlier test, core-ISGs 246, beta-ISG 406, Transcriptome 1905, Genus Microbiome 54")
## [1] "Before outlier test, core-ISGs 246, beta-ISG 406, Transcriptome 1905, Genus Microbiome 54"
########## outlier test ######### by = 2, type 10 get the boolean mask
########## vector
isgs_outlier <- grubbs_df(isgs_rlog, 2, 10)$fdr > 0.05
genesbeta_outlier <- grubbs_df(genesbeta_rlog, 2, 10)$fdr > 0.05
filtered_outlier <- grubbs_df(filtered_rlog, 2, 10)$fdr > 0.05

mibi_outlier <- grubbs_df(mibi, 2, 10)$fdr > 0.05
# subset by outlier test
dim(isgs_rlog[, isgs_outlier])
## [1]  27 203
dim(genesbeta_rlog[, genesbeta_outlier])
## [1]  27 405
dim(filtered_rlog[, filtered_outlier])
## [1]   27 1894
ncol(mibi[, mibi_outlier])
## [1] 44
print("After outlier test, core-ISGs 203, beta-ISG 405, Transcriptome 1894, Genus Microbiome 44")
## [1] "After outlier test, core-ISGs 203, beta-ISG 405, Transcriptome 1894, Genus Microbiome 44"

2 Cross Validation to define penalties

3 Run SmCCNet

3.1 With CD14

########## with CD14 #############3
setwd("~/Documents/gitlab/Omics_Integration/DataProcessed/")
CVDir <- "CD14_Outlier3_Global_100_50_Genus_3_4foldCV/"
dir <- "~/Documents/gitlab/Omics_Integration/DataProcessed/CD14_Outlier3_Global_100_50_Genus_3_4foldCV/"
# edge Cut 0
run_SmCCNet(X1 = filtered_rlog[, filtered_outlier], 
            X2 = mibi[, mibi_outlier],
            Y = CD14,
            l1 = 0.2, 
            l2 = 0.75, 
            s1 = 0.7, 
            s2 = 0.9, 
            weights = NULL,
            # n_na = n_na,
            # NoTrait itself is to control whether to use Y or not 
            NoTrait = FALSE,
            EdgeCut = 0)
## [1] "weights can be NULL or a length 3 vector"

## NULL

## NULL

## NULL

## NULL

## NULL
## [[1]]
##  [1]    2   51   58  112  125  146  147  206  222  332  339  345  351  363
## [15]  374  381  564  629  655  682  684  709  715  947  963  968  976 1051
## [29] 1096 1124 1127 1137 1181 1192 1204 1221 1332 1360 1361 1379 1402 1403
## [43] 1406 1442 1447 1560 1566 1587 1612 1621 1637 1642 1669 1678 1766 1794
## [57] 1843 1936
## 
## [[2]]
##  [1]   18   27   85  102  111  122  187  198  218  221  297  302  372  492
## [15]  540  570  578  591  787  802  821  827  842  888  920  969  981 1035
## [29] 1036 1055 1131 1151 1159 1165 1219 1256 1263 1351 1368 1444 1455 1472
## [43] 1490 1615 1638 1661 1672 1719 1739 1744 1745 1776 1793 1826 1917
## 
## [[3]]
##  [1]   21   46   52  148  160  168  317  380  470  473  484  520  573  604
## [15]  650  738  748  774  783  832  853  855  857  905 1013 1046 1061 1179
## [29] 1232 1315 1325 1430 1437 1439 1538 1564 1625 1630 1647 1676 1690 1695
## [43] 1710 1792 1849 1884 1895 1909 1911 1913 1918 1920 1924 1926 1927 1930
## 
## [[4]]
##  [1]   26   41   50   60   65   88  153  242  278  352  359  419  425  432
## [15]  463  464  515  538  565  627  713  841  854  873  874  916  922  962
## [29]  992 1008 1021 1118 1148 1158 1187 1201 1214 1241 1287 1301 1304 1331
## [43] 1344 1346 1365 1381 1383 1429 1488 1516 1557 1575 1592 1623 1633 1681
## [57] 1688 1692 1728 1797 1801 1816 1825 1837 1921
## 
## [[5]]
##   [1]   40   47   55   71   81  124  132  141  188  203  254  269  275  277
##  [15]  347  356  360  382  441  442  443  504  558  566  585  587  597  605
##  [29]  614  632  653  661  680  694  702  712  727  733  743  751  778  800
##  [43]  835  836  844  860  864  871  882  884  889  902  917  945  999 1007
##  [57] 1014 1077 1107 1169 1205 1244 1260 1295 1316 1324 1336 1367 1380 1421
##  [71] 1428 1456 1489 1495 1498 1499 1509 1527 1561 1576 1593 1604 1610 1622
##  [85] 1717 1718 1733 1741 1762 1769 1781 1790 1811 1832 1848 1850 1873 1874
##  [99] 1876 1887 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907
## [113] 1908 1910 1912 1914 1915 1916 1919 1922 1923 1925 1928 1929 1931 1932
## [127] 1933 1934 1935 1937 1938
######3 edge Cut 0.1 
load(paste0(dir, "SmCCNetWeights.RData"))
edgecut_by(filtered_rlog[, filtered_outlier], mibi[, mibi_outlier], 0.1)
## [1] "Load proper similarity matrix (abar) and modules!"
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."

## NULL

3.2 Without CD14

########## with CD14 #############3
setwd("~/Documents/gitlab/Omics_Integration/DataProcessed/")
CVDir <- "_Outlier1_Global_100_50_Genus_1_4foldCV/"
dir <- "~/Documents/gitlab/Omics_Integration/DataProcessed/_Outlier1_Global_100_50_Genus_1_4foldCV/"
########## without CD14 ###########
run_SmCCNet(X1 = filtered_rlog[, filtered_outlier], 
            X2 = mibi[, mibi_outlier],
            Y = NULL,
            l1 = 0.5, 
            l2 = 0.1, 
            s1 = 0.7, 
            s2 = 0.9, 
            weights = NULL,
            # n_na = n_na,
            # NoTrait itself is to control whether to use Y or not 
            NoTrait = TRUE,
            EdgeCut = 0)
## [1] "weights can be NULL or a length 3 vector"

## NULL

## NULL

## NULL

## NULL

## NULL
## [[1]]
##   [1]    1    4    6    7   13   24   25   32   35   36   44   46   53   56
##  [15]   62   63   66   67   75   77   79   81   82   83   89   93  104  113
##  [29]  114  115  126  128  129  130  131  135  136  137  138  140  142  143
##  [43]  146  153  157  161  172  174  178  180  185  189  193  199  203  204
##  [57]  205  209  212  222  223  226  229  236  238  240  245  246  247  251
##  [71]  253  259  260  268  270  274  282  286  290  294  299  322  325  327
##  [85]  332  335  336  341  343  351  355  356  365  368  369  370  373  376
##  [99]  377  379  383  385  389  391  394  399  400  402  403  405  411  423
## [113]  435  437  442  444  447  448  454  462  472  487  488  496  505  507
## [127]  508  509  512  514  517  522  523  536  543  549  552  554  557  561
## [141]  562  566  567  586  588  589  600  601  605  606  611  612  613  617
## [155]  618  627  631  632  642  643  644  648  654  662  665  668  670  671
## [169]  672  673  675  676  680  693  698  702  706  708  713  718  719  721
## [183]  732  734  738  739  744  750  751  752  763  765  767  770  772  776
## [197]  777  778  783  787  789  791  796  798  805  809  811  815  820  825
## [211]  826  828  829  835  836  843  844  848  852  856  858  861  862  867
## [225]  868  875  877  878  885  899  901  902  903  910  911  912  921  923
## [239]  925  927  929  932  940  953  955  956  961  966  967  974  977  979
## [253]  983  988  989  995  996  997 1001 1009 1010 1011 1012 1015 1018 1025
## [267] 1031 1033 1035 1040 1041 1044 1045 1049 1052 1054 1058 1065 1079 1081
## [281] 1082 1084 1094 1098 1102 1103 1111 1115 1119 1140 1141 1142 1144 1145
## [295] 1147 1152 1157 1160 1161 1163 1170 1172 1174 1182 1184 1188 1207 1208
## [309] 1211 1212 1223 1225 1226 1239 1241 1245 1251 1257 1259 1261 1266 1269
## [323] 1270 1273 1278 1279 1283 1286 1292 1293 1299 1303 1311 1313 1314 1317
## [337] 1320 1328 1330 1336 1338 1340 1343 1347 1348 1353 1354 1367 1371 1375
## [351] 1382 1383 1385 1398 1404 1407 1414 1417 1432 1441 1443 1447 1451 1456
## [365] 1457 1464 1467 1471 1473 1476 1480 1485 1487 1491 1494 1498 1499 1503
## [379] 1510 1511 1513 1516 1521 1530 1532 1540 1544 1548 1556 1562 1563 1569
## [393] 1571 1572 1574 1575 1576 1583 1594 1595 1596 1603 1609 1611 1614 1619
## [407] 1620 1624 1629 1631 1650 1653 1662 1665 1668 1677 1683 1685 1696 1697
## [421] 1703 1705 1708 1711 1717 1718 1721 1724 1730 1741 1746 1747 1750 1757
## [435] 1758 1764 1768 1770 1776 1778 1779 1786 1796 1807 1820 1826 1827 1828
## [449] 1830 1833 1835 1838 1850 1855 1865 1867 1873 1876 1885 1886 1888 1889
## [463] 1891 1896
## 
## [[2]]
##   [1]    2    3   11   17   21   28   30   34   37   47   48   51   55   58
##  [15]   68   69   71   78   80   94   96   97  100  101  105  106  109  112
##  [29]  118  119  124  125  132  139  147  148  154  156  160  164  166  168
##  [43]  169  170  173  175  186  188  201  206  207  214  216  231  232  233
##  [57]  237  248  261  264  269  272  275  283  284  287  289  291  296  302
##  [71]  306  309  310  315  317  320  329  331  339  340  342  345  347  358
##  [85]  360  364  374  381  382  388  404  412  414  417  419  425  429  430
##  [99]  433  436  441  453  457  466  469  473  480  482  484  486  493  500
## [113]  501  504  513  520  525  526  534  548  550  558  563  564  565  569
## [127]  570  577  582  585  587  591  592  596  599  604  607  614  616  621
## [141]  625  629  633  634  646  647  650  653  655  657  658  660  664  666
## [155]  667  679  682  685  689  691  694  695  709  710  712  720  726  727
## [169]  728  733  743  748  755  756  768  769  773  786  793  800  817  831
## [183]  832  841  847  853  855  860  869  871  873  880  882  884  889  898
## [197]  908  916  918  926  941  944  945  948  949  959  963  968  971  972
## [211]  976  980  982  990  994  999 1000 1003 1005 1006 1007 1008 1014 1017
## [225] 1021 1022 1024 1027 1030 1046 1047 1048 1051 1064 1069 1070 1072 1078
## [239] 1080 1083 1092 1093 1096 1097 1104 1121 1124 1125 1126 1137 1138 1143
## [253] 1146 1148 1151 1153 1155 1169 1171 1173 1177 1179 1181 1189 1192 1193
## [267] 1194 1198 1200 1203 1204 1205 1216 1217 1221 1229 1237 1242 1244 1260
## [281] 1267 1272 1276 1280 1282 1284 1290 1306 1310 1316 1318 1325 1345 1356
## [295] 1357 1360 1361 1362 1365 1366 1369 1379 1380 1381 1389 1397 1406 1413
## [309] 1415 1421 1423 1429 1435 1442 1450 1453 1461 1463 1465 1475 1477 1479
## [323] 1481 1482 1489 1495 1496 1505 1507 1509 1514 1522 1527 1528 1538 1539
## [337] 1543 1560 1561 1564 1577 1581 1582 1586 1587 1589 1592 1593 1598 1600
## [351] 1608 1610 1612 1621 1622 1623 1630 1633 1635 1636 1637 1639 1644 1646
## [365] 1647 1652 1659 1669 1674 1675 1676 1678 1680 1689 1690 1692 1704 1709
## [379] 1710 1712 1723 1732 1733 1735 1736 1748 1755 1760 1762 1763 1769 1771
## [393] 1772 1781 1782 1783 1788 1790 1791 1798 1800 1801 1804 1806 1811 1812
## [407] 1816 1817 1829 1839 1842 1843 1848 1849 1857 1859 1860 1863 1870 1874
## [421] 1878 1880 1884 1887 1899
## 
## [[3]]
##   [1]    5   10   14   15   16   18   23   26   33   38   41   45   50   52
##  [15]   54   65   70   72   74   88   92   95   99  103  117  127  134  145
##  [29]  149  159  167  171  182  187  192  195  210  217  220  221  224  225
##  [43]  234  235  242  244  249  250  280  293  298  301  303  304  305  316
##  [57]  319  324  326  338  344  349  353  359  361  367  372  380  384  390
##  [71]  393  395  409  410  424  440  450  456  459  460  463  464  465  476
##  [85]  479  481  492  495  498  503  511  515  519  532  535  540  544  551
##  [99]  556  560  571  575  580  581  583  590  598  602  608  619  624  635
## [113]  637  641  649  651  652  669  686  687  697  699  711  717  722  723
## [127]  725  730  731  737  742  749  761  766  771  775  779  781  782  788
## [141]  794  795  801  802  813  814  819  821  822  830  840  849  850  851
## [155]  863  865  872  874  883  892  894  900  904  906  909  913  914  915
## [169]  920  922  933  934  942  943  957  962  965  969  981  987  992  993
## [183] 1013 1016 1020 1023 1055 1056 1059 1061 1074 1087 1108 1110 1122 1131
## [197] 1139 1158 1164 1167 1168 1187 1199 1201 1202 1219 1224 1228 1232 1233
## [211] 1234 1243 1255 1258 1271 1289 1294 1297 1301 1304 1308 1309 1312 1315
## [225] 1331 1335 1344 1349 1355 1363 1368 1374 1386 1390 1391 1392 1396 1403
## [239] 1412 1422 1431 1436 1439 1454 1470 1472 1478 1484 1488 1490 1492 1493
## [253] 1501 1523 1533 1536 1545 1549 1555 1557 1558 1570 1578 1584 1590 1591
## [267] 1597 1599 1602 1605 1613 1615 1618 1632 1634 1638 1645 1649 1657 1661
## [281] 1670 1672 1681 1684 1686 1688 1691 1693 1699 1701 1706 1707 1715 1719
## [295] 1720 1725 1728 1731 1739 1745 1749 1759 1765 1767 1774 1775 1792 1793
## [309] 1794 1795 1797 1805 1808 1809 1814 1822 1825 1836 1852 1853 1854 1856
## [323] 1858 1869 1871 1872 1883 1893 1894 1908
## 
## [[4]]
##   [1]    9   29   31   40   43   49   59   61   84  107  108  110  121  123
##  [15]  133  141  144  151  158  162  165  176  177  181  184  190  191  194
##  [29]  208  213  215  219  228  239  254  263  277  278  281  285  295  308
##  [43]  311  313  323  337  346  348  350  354  363  378  398  407  408  428
##  [57]  438  439  443  446  449  452  461  467  468  474  475  478  494  502
##  [71]  510  516  530  531  533  537  545  546  547  573  574  597  609  610
##  [85]  620  622  626  638  639  640  659  677  681  684  688  701  703  705
##  [99]  715  740  741  753  758  759  764  774  780  799  818  823  838  859
## [113]  864  879  881  886  891  907  917  928  930  931  938  946  947  954
## [127]  970  973  986 1002 1026 1028 1034 1037 1038 1042 1043 1053 1075 1076
## [141] 1086 1089 1090 1106 1107 1112 1127 1128 1129 1130 1136 1156 1162 1166
## [155] 1183 1206 1213 1231 1240 1246 1248 1254 1263 1265 1268 1275 1277 1285
## [169] 1319 1321 1324 1326 1332 1342 1359 1370 1373 1377 1400 1401 1402 1405
## [183] 1408 1410 1411 1416 1420 1424 1425 1427 1428 1430 1440 1445 1452 1502
## [197] 1506 1515 1517 1520 1525 1526 1529 1535 1541 1547 1551 1553 1559 1566
## [211] 1568 1573 1579 1580 1585 1601 1604 1607 1617 1641 1642 1648 1663 1666
## [225] 1667 1695 1713 1714 1722 1737 1753 1766 1773 1785 1787 1810 1832 1851
## [239] 1864 1877 1881 1882 1890 1923
## 
## [[5]]
##   [1]   12   42  116  152  200  211  265  276  307  321  357  362  396  406
##  [15]  415  418  420  422  426  427  434  445  483  485  528  539  584  623
##  [29]  628  656  683  714  729  754  757  784  790  792  797  807  810  816
##  [43]  837  854  857  870  876  919  952 1032 1050 1067 1071 1073 1099 1101
##  [57] 1113 1116 1150 1159 1175 1185 1190 1196 1197 1214 1220 1227 1230 1235
##  [71] 1250 1252 1262 1264 1281 1388 1399 1418 1419 1433 1434 1437 1474 1486
##  [85] 1497 1537 1554 1565 1640 1658 1679 1702 1740 1742 1777 1780 1815 1821
##  [99] 1831 1837 1844 1845 1879 1901
######3 edge Cut 0.1 
load(paste0(dir, "SmCCNetWeights.RData"))
edgecut_by(filtered_rlog[, filtered_outlier], mibi[, mibi_outlier], 0.1)
## [1] "Load proper similarity matrix (abar) and modules!"

## NULL

## NULL

## NULL
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."

4 Clustering of Clinical Phenotypes

# clin[, c(6:18, 25)] standardized to mean 0 var 1
df <- clin[, c(6:18, 25)]
clinical_names <- c("Blood CD4 T Cell Counts (cells/ul)", "Plasma Viral Load", 
    "Tissue HIV RNA (per CD4 T cell)", "Tissue CD4 T Cell Counts (number/g)", 
    "IL-6 (pg/ml)", "CRP (ug/ml)", "iFABP (pg/ml)", "sCD27 (U/ml)", 
    "CD14 (ng/ml)", "LPS (pg/ml)", "LTA (OD)", base::paste("IFN", 
        "α", sep = ""), base::paste("IFN", "β", sep = ""), 
    "CD4 T cells (% viable CD45+ cells)")
colnames(df) <- clinical_names
density_values_ind(df %>% stack(), "Density plot of clinical phenotypes")
## [1] "A column called group and a column called values"

clin_pearson <- cor_heatmap(df, "pearson", TRUE, "complete", 
    text_size = 3)
## [1] "Use pairwise.complete.obs, and methods from pearson, kendall, spearman"

# clin_pearson <- cor_heatmap(df, 'spearman', TRUE,
# 'complete', text_size = 3)

4.1 CRP is not correlated with LPS/CD14

CRP <- clin %>% select(CRP)
# no missing 
anyNA(CRP)
## [1] FALSE
########## with CRP #############3
setwd("~/Documents/gitlab/Omics_Integration/DataProcessed/")
CVDir <- "CRP_Outlier1_Global_100_50_Genus_1_4foldCV/"
run_SmCCNet(X1 = filtered_rlog[, filtered_outlier], 
            X2 = mibi[, mibi_outlier],
            Y = CRP,
            l1 = 0.1, 
            l2 = 0.05, 
            s1 = 0.7, 
            s2 = 0.9, 
            weights = NULL,
            # n_na = n_na,
            # NoTrait itself is to control whether to use Y or not 
            NoTrait = FALSE,
            EdgeCut = 0)
## [1] "weights can be NULL or a length 3 vector"

## NULL

## NULL

## NULL

## NULL

## NULL

## NULL

## NULL

## NULL

## NULL

## NULL

## NULL
## [[1]]
##  [1]    7   76  215  250  326  501  645  700  760  786  915  953 1063 1067
## [15] 1167 1395 1478 1512 1552 1557 1563 1757 1918
## 
## [[2]]
##  [1]   16  120  183  318  328  334  529  538  987 1181 1188 1322 1774 1926
## 
## [[3]]
##  [1]   17  100  241  355  402  427  503  530  622  785  880 1058 1221 1348
## [15] 1362 1423 1603 1616 1705 1902
## 
## [[4]]
##  [1]   22  176  299  349  492  540  570  802  888 1022 1263 1320 1505 1615
## [15] 1734 1793 1915
## 
## [[5]]
##  [1]   27   74  219  257  343  384  412  467  474  493  510  749  787  804
## [15]  810  812  893 1055 1180 1203 1455 1643 1664 1689 1753 1836 1884 1907
## 
## [[6]]
## [1]   39   95 1073 1210 1333 1346 1594 1669 1938
## 
## [[7]]
##  [1]   44   68   77   83  105  110  150  153  286  448  517  601  686  710
## [15]  795  890  962 1002 1023 1104 1239 1278 1369 1396 1412 1487 1629 1665
## [29] 1670 1703 1728 1786 1806 1859 1895
## 
## [[8]]
##  [1]   52  274  340  459  669  859 1115 1238 1555 1698 1860 1924
## 
## [[9]]
##  [1]   73  245  305  357  425  565  598  635  958 1108 1122 1158 1691 1701
## [15] 1745 1909
## 
## [[10]]
##  [1]  111  302  390  481  608  688  731  866  975 1035 1131 1219 1246 1251
## [15] 1252 1308 1429 1472 1490 1492 1618 1649 1725 1744 1819 1823 1838 1910
## 
## [[11]]
##  [1]  225  969 1043 1072 1077 1134 1393 1409 1459 1803 1919
######3 edge Cut 0.1 
dir <- "~/Documents/gitlab/Omics_Integration/DataProcessed/CRP_Outlier1_Global_100_50_Genus_1_4foldCV/"
load(paste0(dir, "SmCCNetWeights.RData"))
edgecut_by(filtered_rlog[, filtered_outlier], mibi[, mibi_outlier], 0.1)
## [1] "Load proper similarity matrix (abar) and modules!"

## NULL
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."

## NULL

## NULL
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."

## NULL
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."

## NULL
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."

5 LPS

LPS <- clin %>% select(LPS)
n_na <- which(is.na(LPS))
setwd("~/Documents/gitlab/Omics_Integration/DataProcessed/")
CVDir <- "LPS_Outlier1_Global_100_50_Genus_1_4foldCV/"
run_SmCCNet(X1 = filtered_rlog[, filtered_outlier], 
            X2 = mibi[, mibi_outlier],
            Y = LPS,
            l1 = 0.1, 
            l2 = 0.1, 
            s1 = 0.7, 
            s2 = 0.9, 
            weights = NULL,
            n_na = n_na,
            # NoTrait itself is to control whether to use Y or not 
            NoTrait = FALSE,
            EdgeCut = 0)
## [1] "weights can be NULL or a length 3 vector"

## NULL

## NULL

## NULL

## NULL

## NULL

## NULL

## NULL
## [[1]]
##  [1]    5   18   33   41  159  224  248  272  280  303  304  464  476  498
## [15]  532  569  570  689  730  822  874  894  959 1022 1078 1122 1126 1301
## [29] 1479 1536 1545 1558 1592 1659 1704 1725 1816 1908
## 
## [[2]]
##  [1]   28  148  168  169  188  206  275  310  347  374  382  513  587  596
## [15]  650  657  658  712  733  773  783  860  871  889  963  999 1014 1124
## [29] 1148 1192 1194 1221 1237 1306 1316 1442 1495 1507 1509 1538 1577 1612
## [43] 1636 1644 1646 1678 1788 1811 1848 1899
## 
## [[3]]
##  [1]   51  332  344  550  617  916  957  989 1021 1064 1104 1109 1241 1555
## [15] 1563 1759 1819 1826 1905
## 
## [[4]]
##  [1]   73  144  245  349  373  565  602  619  624  635  667  678  717  742
## [15]  813  863  904  906  962  965 1001 1103 1108 1158 1172 1201 1207 1303
## [29] 1488 1492 1516 1575 1613 1724 1745 1825 1909
## 
## [[5]]
##  [1]  106  146  151  254  323  368  478  694  869  881  946 1045 1112 1246
## [15] 1425 1430 1517 1551 1710 1722 1876 1923
## 
## [[6]]
##  [1]  111  524  557  683  784  842 1005 1053 1100 1393 1455 1664 1841 1874
## [15] 1928
## 
## [[7]]
##  [1]  129  137  153  253  335  536  552  670  719  750  811  828  923 1010
## [15] 1144 1152 1161 1184 1494 1602 1629 1807 1896
######3 edge Cut 0.1 
dir <- "~/Documents/gitlab/Omics_Integration/DataProcessed/LPS_Outlier1_Global_100_50_Genus_1_4foldCV/"
load(paste0(dir, "SmCCNetWeights.RData"))
edgecut_by(filtered_rlog[-n_na, filtered_outlier], mibi[-n_na, mibi_outlier], 0.1)
## [1] "Load proper similarity matrix (abar) and modules!"
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."

## NULL
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."
## [1] "No edge passes threshold."